FullRecall is a software that can help you memorize knowledge effectively by scheduling optimal intervals between repetitions. Uses artificial neural network, which gradually grasps your forgetting curve to schedule the best time for an item to come up for a review � a day when you'll be close to forgetting the information.

FullRecall also uses "Power of Question" and CPL (Critical Path Learning) method, based on J. Andrew Shaw's ATOL theory, to maximize learning efficiency. CPL "gets most of its effectiveness from using tight feedback loops and enforcing information accuracy" � FullRecall lets you immediately check if your recall is correct, reinforcing always only the correct answer.

You can also ask FullRecall for "hint" (next character in the answer) or type answer by keyboard and FullRecall gives you instant feedback whether just pressed letter is right.

The software is similar to common flashcard programs: knowledge is stored in question-answer pairs. In review mode you are presented questions, one by one. To every question you think about an answer, and after a while you are confronted with the correct answer. Then you pick a grade to evaluate how well you remembered it. That grade gives FullRecall a feedback.

FullRecall also stores other data (number of total reviews of item, current interval, etc.), given also current grade is able to schedule next optimal review (and later try to learn itself if there was a mistake: if scheduled interval was too long or too short � i.e. if your grade on the next review is below or above "good"). With FullRecall you can learn new things fast, without worrying about repetitions of what you remember � FullRecall assures that even if you forget something � you'll be soon reminded about it.